Optimization of impinging jet ventilation combined with chilled ceiling air-conditioning system based on GA-BP neural network coupled with improved particle swarm optimization
The indoor thermal environment for the impinging jet ventilation combined with radiant ceiling(IJV/RC)air-conditioning system with different operating load conditions was simulated.The GA-BP neural network was applied to develop the predictive models between the operating performance(draught discomfort R PD,temperature difference between the head and ankle level △t,air change efficiency eACE,and average temperature of operation ta)and design variables(supply temperature ts,supply velocity vs,chilled ceiling temperature tc,and room load Qc).The significance of each design variable on the studied operating performance was determined and ranked through correlation analysis.The results show that △t decreases with the increase of vs,but the value of RPD increases accordingly.The increase of tc is helpful for the decrease of △t and RPD,but the value of ta increases.The decrease of ta can be achieved by reducing ts,but the indoor air quality becomes worse.To achieve the goals of providing good indoor air quality and indoor thermal comfort,a multi-objective optimization for the IJV/RC was conducted by applying the improved particle swarm optimization(PSO)algorithm,and the optimal combinations of the studied design variables corresponding to the room loads were developed.The current results can provide theoretical guidance for the design and operation control for the IJV/RC system.
万方数据
维普
